12 citations · 19 across the 3 of their papers we have counts for
5 papers
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow
Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh +1
Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression t…
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models
Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh +1
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques. The deep learning framework incorporates physical constraints…
Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers
Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh +1
Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression t…
Local analysis of the clustering, velocities and accelerations of particles settling in turbulence
Mohammadreza Momenifar, Andrew D. Bragg
Using 3D Vorono\text{ï} analysis, we explore the local dynamics of small, settling, inertial particles in isotropic turbulence using Direct Numerical Simulations (DNS). We independ…
The influence of Reynolds and Froude number on the motion of settling, bidisperse inertial particles in turbulence
Mohammadreza Momenifar, Rohit Dhariwal, Andrew D. Bragg
Using Direct Numerical Simulations (DNS), we examine the effects of Taylor Reynolds number, , and Froude number, , on the motion of settling, bidisperse inertial particles…